InĀ [10]:
import pandas as pd
import numpy as np
from PlateAnalysis import PlateDataset
import matplotlib.pyplot as plt
import re

ConcMap_Plate1 = dict(zip(range(1,25),[7.58]*6 + \
                       [15.161]*6 + \
                       [7.58]*6 + \
                       [15.161]*6))

ConcMap_Plate2 = dict(zip(range(1,25),[8.781889]*6 + \
                       [16.051088]*6 + \
                       [8.781889]*6 + \
                       [16.051088]*6))

VolMap = dict(zip(range(1,25),[20]*12 + \
                       [40]*12 ))

def PlotAndGetMerics(path,ConcMap, VolMap,triton):
    Plate = PlateDataset(path)

    metrics = pd.DataFrame([],columns = ['Percent Triton','Vol','[Protein]','Kd',\
                                         'Vmax','R_sq','Noise','Degree Shift'])

    for j in range(1,25):

        vol = VolMap[j]
        protConc = ConcMap[j]

        print('Triton: {}\t Working Volume: {}\t Protein Conc: {}'.format(triton,vol,protConc))

        NormalizedTraces, DifferenceSpec, DiffDiff, DegShift= Plate.AnalysisPipeline_1(j)
        Plate.PlotTrace(NormalizedTraces)
        Plate.PlotTrace(DifferenceSpec)
        K,percentDMSO = 3, 5
        concs = Plate.CalculateCompoundConcs(K,vol,percentDMSO)
        km, vmax, loss = Plate.FitMichaelisMenten(concs, DiffDiff)
        Plate.PlotMichaelesMenten(DiffDiff,concs,\
                              km, vmax, loss,\
                              'Plate: {}'.format(Plate.plate_data))
        noise = Plate.Noise(NormalizedTraces)

        temp = pd.DataFrame([[triton,vol,protConc,km,vmax,1-loss, noise,DegShift]],\
                                      columns = ['Percent Triton','Vol','[Protein]',\
                                                 'Kd','Vmax','R_sq','Noise','Degree Shift'])
        metrics = metrics.append(temp,sort = True)

    metrics.reset_index(inplace = True,drop=True)
    return metrics

KPiMetrics = PlotAndGetMerics('20191217_Kpi.CSV',ConcMap_Plate1, VolMap,triton=0)
TritonMetrics = PlotAndGetMerics('20191217_KpiTriton.CSV',ConcMap_Plate2, VolMap,triton=1)

output = KPiMetrics.append(TritonMetrics)
output.reset_index(inplace = True,drop=True)
output.to_csv('20191217_PlateMetrics.csv')
Triton: 0	 Working Volume: 20	 Protein Conc: 7.58
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InĀ [3]:
output
Out[3]:
Degree Shift Kd Noise Percent Triton R_sq Vmax Vol [Protein]
0 0.053893 35.181252 0.002478 0 0.986295 0.048889 20 7.580
1 0.141816 26.648445 0.002868 0 0.988172 0.042622 20 7.580
2 0.687385 40.671169 0.002401 0 0.984933 0.046520 20 7.580
3 0.378226 32.401878 0.001664 0 0.986372 0.044867 20 7.580
4 0.095330 36.731842 0.004029 0 0.985891 0.048033 20 7.580
5 0.041183 28.304813 0.000780 0 0.987835 0.043561 20 7.580
6 1.146147 44.052696 0.004773 0 0.983936 0.061841 20 15.161
7 0.988525 38.042732 0.006426 0 0.985202 0.058882 20 15.161
8 0.725714 47.100773 0.004664 0 0.983296 0.065113 20 15.161
9 6.796467 29.174292 0.007526 0 0.986320 0.054498 20 15.161
10 0.606433 48.305756 0.002476 0 0.982820 0.065121 20 15.161
11 0.406740 31.939753 0.000552 0 0.986592 0.061577 20 15.161
12 37.373332 33.859905 0.008629 0 0.984837 0.071640 40 7.580
13 53.105473 30.294701 0.008277 0 0.986816 0.082606 40 7.580
14 0.425360 39.068981 0.007535 0 0.985289 0.099862 40 7.580
15 0.822582 36.524937 0.011298 0 0.985626 0.090260 40 7.580
16 0.169426 37.749489 0.002936 0 0.985674 0.100008 40 7.580
17 0.840988 35.815987 0.011929 0 0.985956 0.094761 40 7.580
18 0.703400 46.252796 0.003965 0 0.983446 0.138271 40 15.161
19 1.422770 37.669594 0.019740 0 0.985562 0.115076 40 15.161
20 0.157239 46.168076 0.002759 0 0.983417 0.141027 40 15.161
21 0.906201 41.108604 0.010949 0 0.984719 0.129187 40 15.161
22 0.298634 45.116188 0.001927 0 0.983716 0.139201 40 15.161
23 1.364912 39.394058 0.010267 0 0.985232 0.125428 40 15.161
24 0.040912 64.956848 0.006512 1 0.975304 0.046469 20 7.580
25 19.477697 57.979454 0.001941 1 0.977858 0.032104 20 7.580
26 0.074219 64.043594 0.001928 1 0.975811 0.042776 20 7.580
27 0.545350 50.300808 0.001923 1 0.980644 0.033484 20 7.580
28 0.151554 66.824348 0.002075 1 0.974496 0.040538 20 7.580
29 1.341194 55.549305 0.003440 1 0.978284 0.036560 20 7.580
30 4.154818 87.505753 0.012627 1 0.961555 0.117336 20 15.161
31 2.144766 62.301949 0.010350 1 0.974610 0.075637 20 15.161
32 1.047660 78.689346 0.006846 1 0.967535 0.107072 20 15.161
33 105.646114 53.223331 0.009548 1 0.975925 0.080818 20 15.161
34 1.238241 75.658134 0.007935 1 0.969333 0.111701 20 15.161
35 47.600508 52.889118 0.014459 1 0.976712 0.066880 20 15.161
36 0.090038 55.568256 0.003556 1 0.979589 0.050309 40 7.580
37 1.098151 43.557678 0.008495 1 0.982936 0.047178 40 7.580
38 0.244774 56.585217 0.003317 1 0.979241 0.048932 40 7.580
39 1.957037 51.296253 0.009354 1 0.980393 0.045646 40 7.580
40 0.121938 55.399578 0.005670 1 0.979459 0.052411 40 7.580
41 9.667485 50.459568 0.006497 1 0.980873 0.043229 40 7.580
42 0.332498 65.218826 0.003616 1 0.974949 0.133547 40 15.161
43 5.603914 43.559280 0.011827 1 0.981176 0.099589 40 15.161
44 0.453134 67.206566 0.002957 1 0.974263 0.135396 40 15.161
45 9.181211 43.670204 0.013744 1 0.981206 0.102481 40 15.161
46 0.162697 67.814102 0.002314 1 0.973911 0.134242 40 15.161
47 5.284549 42.910038 0.014445 1 0.981255 0.101502 40 15.161
InĀ [4]:
import seaborn as sns

g = sns.pairplot(output.dropna())
plt.show()
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InĀ [13]:
from PathLength import PlatePathlength

KPi_PlatePathLength = PlatePathlength('20191217_pathlength_1.CSV',0.1)
Triton_PlatePathLength = PlatePathlength('20191217_pathlength_2.CSV',0.1)

KPi_PlatePathLength.PlotPlatePathlenghs()
Triton_PlatePathLength.PlotPlatePathlenghs()
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